Discrete probability distribution

The expected value of R is ∑(r(40 - r)) from 1 to 40 divided by ∑(40 - r) from 1 to 40.So you need to evaluate the sum of (r(40 - r)) and the sum of (40 - r).Do you know how to do that?
  • #1
ineedmunchies
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Homework Statement


The problem is as shown in the attatchment.


Homework Equations


The relevant equations are also given in the attatchment.


The Attempt at a Solution


My problem is how to adapt the given formula in order to find the sum of the function k(40-r)

Do i use the formula for 1 to 40, then 1 to 20 and subtract?

Even just a hint in the right direction would be useful. This sort of stuff has never been my strong point.
 

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  • #2
Think i might have got it.

k(1[tex]\rightarrow[/tex]40[tex]\sum(40-r)[/tex] - 1[tex]\rightarrow[/tex]19[tex]\sum(40-r)[/tex]) + 20k = 1

k(40*40 - [tex]\frac{40(41)}{2}[/tex] - (19*20 - [tex]\frac{19(20)}{2}[/tex])) = 1

230k =1
k = 1/230EDIT sorry its a bit messy, don't know how to notate the limits for the summation properly.
 
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  • #3
How did

[tex]\sum_{r=1}^{19}(40-r) = (19)(20) - \frac{(19)(20)}{2}[/tex]

The part I don't understand is the (19)(20). And what happened to the 20k? Should it be 20k?
 
  • #4
Hi ineedmunchies! :smile:

You've made it very complicated. :confused:

∑(40 - r) from 20 to 40 is simply:

20 + 19 + … + 1 + 0.

Just sum that! :smile:
 
  • #5
Tedjn said:
How did

[tex]\sum_{r=1}^{19}(40-r) = (19)(20) - \frac{(19)(20)}{2}[/tex]

The part I don't understand is the (19)(20). And what happened to the 20k? Should it be 20k?

I'm pretty sure that should be 19*40 and the 20k should be taken into account too. But as tim said, I've made it too complicated.
 
  • #6
Tim did make a good observation. Make sure, however, that you don't use 20k. What should it be instead?
 
  • #7
So can anyone help on how to get the expected value of R?

I think you multiply the two functions by r and then work out the sum over the ranges. Not entirely sure.

Oh and does anybody agree with my value of 1/230 for k??EDIT: sorry didn't see your post tedjn.

Should it be 20*19k instead??
 
  • #8
I think you have the right idea, but how many numbers are between 0 and 19 inclusive?
 
  • #9
ahhh silly little mistake again. twenty, ok getting there slowly.

1 = 20*20k + (20*21/2)k = 610k
k=1/610 hows that look?
 
  • #10
Yeah, that looks good.
 
  • #11
ineedmunchies said:
So can anyone help on how to get the expected value of R?

I think you multiply the two functions by r and then work out the sum over the ranges.
Hi ineedmunchies! :smile:

Yes, that's right!
 

FAQ: Discrete probability distribution

What is a discrete probability distribution?

A discrete probability distribution is a statistical concept that shows the probabilities of all possible outcomes of a discrete random variable. It is represented by a table or a graph, where the sum of all probabilities equals 1.

What is a random variable?

A random variable is a variable whose value is determined by the outcome of a random event. In a discrete probability distribution, the random variable is discrete and can only take on a finite or countable number of values.

What is the difference between a discrete and a continuous probability distribution?

A discrete probability distribution deals with a discrete random variable, meaning the possible values are distinct and countable. A continuous probability distribution, on the other hand, deals with a continuous random variable, meaning the possible values are uncountable and can take on any value within a given range.

What is the expected value of a discrete probability distribution?

The expected value of a discrete probability distribution is the long-term average outcome if the experiment is repeated an infinite number of times. It is calculated by taking the sum of all possible outcomes multiplied by their corresponding probabilities.

What is the role of a probability mass function in a discrete probability distribution?

A probability mass function (PMF) is a function that maps each possible outcome of a discrete random variable to its corresponding probability. It is used to calculate the probabilities of individual outcomes and is essential in determining the shape and characteristics of a discrete probability distribution.

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